**Development result on a measured-state-dependent two-edge surrogate.** A--B,
combined task error after matching at least 6 s of nominal training time across
switching periods. C--D, corresponding gate-space cycle span. The local affine
bias fields are inferred from the released Dillavou et al. drift traces; they
are not independent hardware measurements. Frozen constant calibration and
SDIL receive the same 660/2040 neutral observations in the two task pairs,
respectively, before learning. SDIL removes essentially the entire modeled
raw-to-oracle gap and beats frozen constant calibration at every period. A
constant estimator can also reach oracle performance when allowed another
240--6000 online neutral observations per run. Most importantly, the ideal
leading-order overclamping analogue reaches zero error without neutral
observations and beats SDIL throughout. Thus this result verifies the local
mechanism and observation tradeoff but does not pass the physical strong-
baseline gate or demonstrate correction on hardware.